A new multivariate standard addition strategy for stripping voltammetric electronic tongues: Application to the determination of Tl(I) and In(III) in samples with complex matrices

Pérez‐Ràfols, Clara; Puy-Llovera, Jaume; Serrano, Núria; Ariño, Cristina; Esteban, Miquel; Dı́az-Cruz, José Manuel

doi:10.1016/j.talanta.2018.09.035

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…It is a strategy developed to solve complex matrices and overlapped peaks issues in tonic water samples spiked with Tl + and In 3+ . A relative error calculated from the PLS model varied from 29.7% to 45.3% using external calibration, which fallen sharply to 0.5%-2.3% for the proposed multivariate standard addition [55]. This postulate glimpses to solve situations in which the overlapping of peaks occur in complex matrices that cannot be simulated nor externally calibrated.…”

Section: E-tongues Applied In Heavy Metals Detectionmentioning

confidence: 86%

Heavy Metal/Toxins Detection Using Electronic Tongues

2019

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The growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.

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Section: E-tongues Applied In Heavy Metals Detectionmentioning

confidence: 86%

Heavy Metal/Toxins Detection Using Electronic Tongues

2019

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“…This was significant as it not only made electrodes work stably but also reduced the fluctuation of electrodes potential by changing the states of the electrode conditioning [36]. Using MSAM reduces the conjunction potential and drift potential of electrodes [42], and the ionic strength [20], [55]. Figure 5 shows that the distributions of electrodes potentials were more correlative with the phosphate concentration than those of direction method sampling (figure 4), especially in a high range of concentration in which the electrode reaction with phosphate was more stable [36].…”

Section: Discussionmentioning

confidence: 99%

“…PLSR and GPR, reported in previous studies [20], [57]- [59], were used to process enriched data and contributed towards the improvement in phosphate sensing performance. Although the enriched data improved the performance of the models significantly (see table 7), they also increased the nonlinearity.…”

Section: Discussionmentioning

confidence: 99%

“…For example, multivariate standard addition method (MSAM) and principal component analysis (PCA) or partial least squares regression (PLSR) model to overcome problems such as potential drift, ionic strength, and matrix effect etc. [15]- [20]. Currently, the multilayer perceptron neural network structure and learning performance have been substantially improved through the error back propagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Standard Addition Cobalt Electrochemistry Data Fusion for Determining Phosphate Concentration in Hydroponic Solution

et al. 2020

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Phosphate is one of the major elements affecting agricultural production. The accurate determination of phosphate concentration essential for plant growth, especially in a hydroponics system, allows regulating the balanced and suitable range set of nutrients to plants efficiently. This study proposed a data fusion model based on 70 samples for calibration and 30 samples for predicting concentrations of phosphate in an eggplant nutrient solution. Three multivariate analysis methods i.e. partial least squares model (PLS), Gaussian process regression (GPR), and artificial neural network (ANN) were studied and compared for their performance efficiencies. The results showed that combining the multivariate standard addition method (MSAM) in acquiring data from cobalt electrodes and ANN data fusion model came up with satisfactory outcomes. Both the method provided good performance with R 2 values of 0.98 and 0.96, and the root mean square error (RMSE) of 50 and 66 mg. L −1 respectively in calibration and evaluation tests. These values were much higher than those of conventional processing techniques. Moreover, the normal direct calibration method in acquisition signal from cobalt electrodes was also applied, which provided R 2 values of 0.7 to 0.8. These high values are sufficient for development to measure phosphate concentration in hydroponic solutions. INDEX TERMS Phosphate sensing, multi-sensor data fusion, multivariate standard addition method (MSAM), partial least squares model (PLS), Gaussian process regression (GPR), neural network-ANN.

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“…Some research groups prefer arrays of bare metals such as silver, gold, platinum, or iridium combined with high and low amplitude pulsed signals at different frequencies [15], whereas other groups select electrode substrates chemically modified with substances that present certain affinity for the target species combined with the potential scans typically used in voltammetry (e.g., linear sweep, differential pulse, square wave). In the last case, modifications can be made on many substrates such as carbon paste [16], graphite–epoxy composites [17], glassy carbon [18] or screen-printed electrodes [19].…”

Section: Introductionmentioning

confidence: 99%